A major focus of our laboratory seeks to understand the relationship between what is observed in functional neuroimaging studies and the underlying neural dynamics. To do this, we had previously constructed a large-scale computer model of neuronal dynamics that performs a visual object-matching task similar to those designed for PET/fMRI studies (reviewed in Horwitz and Husain, Handbook of Brain Connectivity, in press). We extended the model so that it could also simulate auditory processing, thus allowing us to investigate the neural basis of auditory object processing in the cerebral cortex (reviewed in Husain & Horwitz, J. Physiol-Paris, 2006). This model relates neuronal dynamics of cortical processing of auditory spectrotemporal patterns to fMRI data.[unreadable] [unreadable] Environmentally relevant auditory stimuli are often composed of long-duration tonal patterns (e.g., multisyllabic words, short sentences, melodies). Manipulation of those patterns by the brain requires working memory to temporarily store the segments of the pattern and integrate them into a percept. To understand the neural basis of how this is accomplished, we extended the model of auditory recognition of short-duration tonal patterns described above. A memory buffer and a gating module were added. The memory buffer increased the storage capacity; the gating module distributed the segments of the input pattern to separate locations of the memory buffer in an orderly fashion, allowing a subsequent comparison of the stored segments against the segments of a second pattern. Current simulations show that the extended model performs match and mismatch of sequences of long-duration tonal patterns. We conducted an fMRI experiment using the same stimuli as employed in the simulations and found areas in the prefrontal cortex that are likely candidate brain areas for the new modules of the extended model.[unreadable] [unreadable] Recently, we have used this auditory neural model and fMRI to investigate the neural mechanisms responsible for tinnitus. The working hypothesis is that a network of brain regions, from auditory processing areas to emotional processing areas contributes to, and modulates, tinnitus perception. An implicit goal of the experiments and modeling is to contrast the neural differences between participants with hearing loss and tinnitus from those with hearing loss but without tinnitus. Preliminary analysis of the fMRI data suggests that the brain activation patterns of tinnitus subjects while listening to music or actively discriminating simple sounds differ from activation patterns of subjects from the control groups. Preliminary results from simulating different possible neural mechanism underlying tinnitus generation using a large-scale neural network model suggest that an increase in excitability and a decrease in the auditory cortex input threshold is the likeliest mechanism of tinnitus (Husain, in Tinnitus: Pathophysiology and Treatment, Progress in Brain Research Series, in press).[unreadable] [unreadable] Functional neuroimaging data, particularly that acquired using fMRI, can be used to assess how different brain regions interact with one another during the performance of cognitive tasks. The quantities that characterize these interactions are called functional or effective connectivity. The neurobiological substrates of functional and effective connectivity are, however, uncertain. Functional connectivity is computed as the correlation between interregional activities, whereas effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity. We proposed a method that, given a set of regions, performs partial correlation analysis. This method provides an approach to effective connectivity that is data driven, in the sense that it does not require any prior information regarding the anatomical or functional connections. To demonstrate the practical relevance of partial correlation analysis for effective connectivity investigation, we reanalyzed data previously published and showed that partial correlation analysis can hint at which effective connections are structuring the interactions (Marrelec et al., Magn. Res. Imaging, in press).[unreadable] [unreadable] Many brain disorders seem to result from alterations in the strength of anatomical connectivity between different brain regions. These disorders have been studied using functional neuroimaging and techniques that evaluate interregional effective connectivity. The purpose of this study was to investigate how the diminution of brain anatomical connectivity can be revealed by the change of brain functional interactions. To do this, we applied simulated fMRI data from a biologically realistic neural network model to structural equation modeling SEM, a well known statistical technique for brain effective connectivity analysis. We used fMRI data generated by a large-scale neural network performing a visual delayed match-to-sample (DMS) task. Two subject groups were simulated: a normal subject group and a patient group for whom the strength of one anatomical connection was reduced to 20% of its normal value. The model comparison between normal and patient groups showed a significant difference between groups. Specifically, the weakened anatomical connection in patients manifested itself as a weakened effective connection. This means that SEM has the potential for finding such connections in real brain fMRI data. But importantly, the feedback pathways which were downstream from the damaged anatomical connection for patients were also significantly reduced. This means that a weakened effective connection cannot necessarily be attributed to a weakened anatomical connection.[unreadable] [unreadable] Remembering associations between names and objects is fundamental to language (the use of neuroimaging to study language is reviewed in Horwitz and Wise, in Handbook of the Neuroscience of Language, in press). We examined the effect of retention interval and language on the neural correlates of memory for arbitrary picture-sound associations and naming using fMRI and a paired associates (PA) task. Adults learned unique picture-sound associations. Ten animal photographs were filtered to be unintelligible but recognizable once shown the original image and were paired with sine wave speech versions of the depicted animal's name. Ten control pairs were also included. To assess the retention interval effect on PA memory, we collected fMRI images while subjects, unaware of the embedded animals, performed a delayed PA task at two time points: immediately after training (D0) and twenty-eight days after training (D28). Contrasting average delay period response, D0 was larger in posterior occipital and parietal cortices whereas D28 was larger bilaterally in temporal and frontal areas. To assess the language effect on PA memory retrieval, subjects were subsequently trained on D28 to resolve the animals in the images and sounds and were then imaged performing the PA task in this informed state. Though some consolidation of PA memory to lateral temporal cortex appears to occur by D28, medial temporal dependence remains prominent, possibly due to task difficulty or recoding. Language use primarily reversed or increased lateralization of several foci in audio-visual association areas suggesting that naming relies on possibly separate but similar neural circuits to visual-auditory PA memory. These data will be used to expand our auditory and visual models so that they can perform the PA task (a review of the efforts at modeling memory processing is found in Horwitz and Smith, Methods, in press).